Note: Descriptions are shown in the official language in which they were submitted.
CA 02585824 2007-03-29
BREATHING SOUND ANALYSIS FOR DETECTION OF SLEEP
APNEAIHYPOPNEA EVENTS
This invention relates to an apparatus for use in breathing sound
analysis for detection of sleep apnea/hypopnea events.
This application is related to a co-pending Application filed on the
same day as this application under Attomey Docket No. 84201-1502 and entitled
BREATHING SOUND ANALYSIS FOR ESTIMATION OF AIRFLOW RATE
BACKGROUNDOF THE INVENTICIN
Sleep apnea syndrome (SAS) is a common respiratory disorder. By
definition, apnea is the cessation of airflow to the lungs (usually during
sleep) which
lasts for at least 10 seconds. Polysomnography (PSG) during the entire night
is
currently the only reliable diagnostic method of sleep apnea. The standard PSG
consists of recording various physiological parameters including EEG, ECG, EMG
of
chins and legs, nasal airflow, electro-oculogram (EOG), abdominal and thoracic
movements, and blood oxygen saturation (Sa02). However, the high cost of the
system, discomfort of the electrodes connecting to the body and the high
amount of
information required to be analyzed are the main disadvantages of this method.
Several researchers have tried to detect apnea using smaller number
of features such as airflow, Sa02 and respiratory effort. Also, in a recent
study an
acoustical method based on lung sounds power at different frequency ranges was
proposed for apnea and snore detection with a sensitivity of about 77% at best
situation. In the other studies airflow was measured using either face masks
or nasal
CA 02585824 2007-03-29
2
cannulae and its cessation was detected as the main sign of apnea. However,
face
mask results in unavoidable changes in breathing pattern and also its
application,is
a challenge when studying children with neurological impairments. On the other
hand, usage of nasal cannulae is highly questionable due to the leakage of
airflow
and possibility of breathing through the mouth.
Sleep apnea syndrome (SAS) can become very serious. It is most
common in obese people, people with high blood pressure, people with narrowed
airway due to tonsils or adenoids, people with stroke or brain injuries, and
smokers.
Sleep apnea occurs two to three times more often in the elderly and also more
in
males than in females. It can cause cardiovascular problems, daytime fatigue,
irritability, lack of concentration and sleepiness causing accidents. Most
people with
obstructive sleep apnea snore; but not everybody that snores has sleep apnea.
Analysis of breathing sounds from a patient for determination of sleep
apnea and/or hypopnea is proposed in a paper entitled "Validation of a New
System
of Tracheal sound Analysis for the diagnosis of Sleep Apnea-Hypopnea Syndrome"
by Nakano et al in "SLEEP" Vo127 No. 5 published in 2004. This constitutes a
research paper postulating that sleep apnea can be detected by breathing sound
analysis but providing no practical details for a system which may be used 'in
practise. It is believed that no further work has been published and no
commercial
machine has arisen from this paper.
US Patent 6,290,654 (Karakasoglu) issued. September 1e 2001
discloses an apparatus for analyzing sounds to estimate airflow for the
purposes of
CA 02585824 2007-03-29
3
detecting apnea events. It then uses a pattem recognition circuitry to detect
patterns
indicative of an upcoming apnea event. In this patent two microphones located
close to the patient's face and on patient's trachea are used to record
respiratory
sounds and ambient noise, respectively. The third sensor records oxygen
saturation. Two methods based on adaptive filter were applied to remove the
ambient noise from respiratory sounds. Then the signal was band-pass filtered
and
used for airflow estimat;on. The estimated airflow signals from two sensors
and
oxygen saturation data were fed to a wavelet filter to extract respiratory
features.
Then the extracted features along with the logarithm values of the estimated
airflow,
signals from two sensors and oxygen saturation sensor were applied to a neural
network to find normal and abnormal respiratory pattems. tn the next step k-
mans
classifler was used to find apnea and hypopnea events in the abnormal
respiratory
patterns. In this patent after removing background noise from the signals, the
signals are fed to a filter bank which consists of a series of filters in the
range of
3001500 with bandwidth of 140Hz and then the output of the filter with higher
signal
to noise ration is selected for flow estimation. Respiratory sounds data below
30QHz
are crucial for flow estimation during shallow breathing which occurs during
sleep.
Finally in this patent both acoustical signals and oxygen saturation data are
used for
apnea detection.
In US Patent 5.797.852 (Karakasoglu) assigned to Local Silence Inc
filed 1993 and issued 1998 and now expired is disclosed an apparatus for
detecting
sleep apnea using a first microphone for detection of breathing sounds and a
second
CA 02585824 2007-03-29
4
microphone for cancelling ambient sounds. This patent apparently lead to
release of
a machine called "Silent Night" which was approved by FDA in 1997 but
apparently
is no longer available. In this patent a system comprised of two microphones
is
proposed for apnea detecfion. The first microphone is placed near the nose and
mouth of the subject to record inhaling and exhaling sounds and the second
microphone is positioned in the air near the patient to record ambient noise.
The
data of the second microphone is used to remove ambient noise from the first
signal
by means of adaptive fifkering. Then the filtered signal is applied to a model
for
estimating flow and classifying as apnea or normal breathing. The way the
patent
proposes to record signals it is obvious that the author has never done any
experiment with the respiratory sounds. In this patent the main signal is
recorded
from a place "near" mouth and nose. This is a very vague description of the
microphone location and will not record any respiratory sounds especially at
low flow
rates, which is the rate during sleep usually.
A related Patent 5.$44.996 (Enzmann and Karakasogiu) issued 1998
to Sleep Solutions Inc is directed to reducing snoring sounds by counteracting
the
sounds with negative sounds. This Assignee has a sleep apnea detection system
currently on sale called NovaSom QSG but this uses sensors of a conventional
nature and does not attempt to analyze breathing sounds. In this patent a
method
for removing snoring sounds is proposed. The patent consists of two
microphones
and a speaker. The first microphone is placed near the noise source to reCord
the
noise. The recorded noise is analyzed to generate a signal with opposite
amplitude
CA 02585824 2007-03-29
and sign and played by the speaker to neutralize noise in the second position.
In
order to decrease the error, the second microphone is piaoed in the second
position
to get the overall signal and noise and compensate for the noise. This patent
is
about noise cancellation and specially snoring sound, not apnea detection or
5 screening, The first microphone which provides the primary signal is placed
near
the head of the subject and not in a place suitable for recording respiratory
sounds.
Nothing Is done for flow estimation or apnea detection.
US Patent 6,241,683 (Macklem) issued June 5th 2001 discloses a
method for estimating air flow from breathing sounds where the system
determines
times when sounds are too low to make an accurate determination and uses an
interpolation method to fill in the information in these times. Such an
arrangement is
of course of no value in detecting apnea or hypopnea since it accepts that the
infomnation in these times is inaccurate. In this patent tracheal sound is
used for
estimation of flow ventilation parameters. Although they mentioned their
method can
be used to detect several respiratory diseases including sleep apnea, their
main
focus is not on the sleep apnea detection by acoustical means. They do not
menfion
how they are going to remove ambient noise and snoring sounds from the
recordings nor the use of oxygen saturation data for further investigations.
Also they
have used wired microphone pl8ced over trachea. The other difference is in the
signal processing method applied for flow estimation. They are using average
power of tracheal sound for flow estimation but it has been shown that average
power can not follow flow changes accurately. Also in this study the recorded
CA 02585824 2007-03-29
6
respiratory sounds are bandpass filten+d in the range of [200-1000]Hz to
remove
heart sounds, which results in low accuracy in estimating flow during shallow
breathing.
US Patent 6,666.830 (Lehrman) issued December 23"' 2003 discloses
an apparatus for analyzing sounds to detect pattems indicative of an upcoming
apnea event. It does not attempt to determine an estimate of air flow to
actually
locate an apnea event but instead attempts to detect changes in sound caused
by
changes in airflow pattems through the air passages of the patient. In this
patent
four microphones are located on a collar around the neck to measure
respiratory
sounds and a sensor is placed close to nostrils to measure airflow. The
airflow
signal is used to find breathing pattem and the microphones signals are
filtered and
analyzed to find the onset of apnea event. In this patent snoring and ambient
noise
detection has not been discussed. This arrangement does not estimate flow from
respiratory sounds so that they cannot calculate respiratory parameters such
as
respiratory volume based on flow data.
Polysomnography (PSG) testing during the entire night is currently the
accepted gold standard diagnostic method of sleep apnea. The standard PSG
consists of recording various physiological parameters inciuding EEG, ECG, EMG
of
chins and fegs, nasal airflow, electro-oculogram (EOG), abdominal and thoracic
movements, and blood oxygen saturation (Sa02) and usually snoring sounds.
However, the high cost of the system, discomfort of the electrodes connecting
to the
body and the high amount of information required to be analyzed are the major
CA 02585824 2007-03-29
7
disadvantages of this testing method.
This complexity of the monitoring system causes a very long waiting
list for patients to go through a sleep study. Hence healthcare providers and
payers
are seeking altemative methods, portable devices and automatedrntelligent
systems
in which sleep apnea testing can be done at the home of patient but with the
same
diagnostic values.
SUMMARY OF THE INVENTION
It is one object of the invention to provide an apparatus for use in
breathing sound analysis for detection of sleep apnea/hypopnea events.
According to the invention there is provided an apparatus for use in
use in analysis of breathing of a patient during sleep for detection of apnea
comprising:
a microphone arranged to be located on the patient for detecting
breathing sounds;
a transmitter at the patient for transmitting signals from the breathing
sounds to a remote location;
a detector module for receiving and analyzing the signals to extract
data relating to the breathing;
the detector module being arranged to analyze the signals to generate
an estimate of air flow while extracting extraneous sounds related to snoring
and/or
heart;
the detector module being arranged to analyze the estimate of air flow
CA 02585824 2007-03-29
8
to detect periods of apnea andlor hypopnea;
and a display of the detected apnealhypopnea episodes and related
information for a clinician.
Preferably there is provided additionally a microphone to collect
extraneous sounds from the patient and a sensor for oximetric signals for
transmission to the remote receiver.
Preferably the transmitter is arranged to compress data for
transmission.
Preferably the remote receiver and detector module are arranged to
receive signals from a plurality of transmitters at different locations
through an
organizer module.
Preferably the detector module connects to an interface for
transmission of data to different locations.
Preferably there is provided a second microphone arranged to receive
sounds from the patient in the vicinity of the patient so as to be sensitive
to snoring
and wherein the detector module is arranged to use adaptive filtering to
extract the
signals relating to the snoring from the signals including both the breathing
sounds
and the snoring sounds.
Preferably the detector module is arranged to cancel heart sounds.
Preferably the detector module is arranged to calculate a function
representing the range of the signal or the entropy of the signal providing an
estimate of air flow during breathing.
CA 02585824 2007-03-29
9
Preferably the detector module is arranged to cancel heart sounds
from the function.
In one preferred method, the function is the range of the signal which Is
defined as the log of the difference between minimum and maximum of the signal
within each short window (i.e. 100 ms) of data.
In another preferred method, the function is the entropy of the signal
which is defined by the following formula:
N
H(P) nr 109 P,+
where p, is the probability distribution function of the ;* event.
Preferably sieep apnea and/or hypopnea is detected by comparing the
function to a threshold of estimated air flow.
Preferably the threshold is defined as the minimum of the Otsu's
threshold and the average value of the function within each data window where
the
Otsu's threshold is defined as the threshold which maximizes the between class
variance.
Preferably the display includes a display of airflow versus time Is
plotted with apnea and hypopnea episodes marked in.
Preferably the display includes oximetry data plotted in association
with the estimated airflow.
Preferably the display is capable of zoom-in and zoom-out functions in
the same window for both airflow and oximetry data sirnuitaneously.
Preferably the display is capable of playing the breathing and snoring
CA 02585824 2007-03-29
sounds in any zoomed-in or zoomed-out data window.
Preferably the display is capable of playing the breathing and snoring
sounds in any zoomed-in or zoomed-out data window.
Preferably the display is capable of displaying the extracted
5 information about the frequency and duration of apnea/hypopnea episodes, and
their
association with the level of oximetry data in a separate window for the
clinician.
Preferably the microphone is arranged to be located in the ear of the
patient.
Preferably the microphone in the ear includes a transmitter arranged
10 for wirefess transmission to a reoeiver.
The apparatus described hereinafter uses tracheal sound entropy to
detect apnea occurrence during breathing.
The apparatus described hereinafter provides an integrated system to
acquire, de-noise, analyze the tracheal respiratory sounds, estimate airflow
acoustically, detect apnea episodes, report the duration and frequency of
apnea,
and to use wireless technology to transfer data to a remote clinical
diagnostic center.
In the present invention the sounds are recorded from inside the ear.
In this patent the snore detection, air volume estimation, difference between
apnea
and hypopnea and recording of oximetry data as a complementary data were not
considered, while in our study they are. The flow estimation method is also
different.
The most important difference in relation to the present invention is that the
microphone and oxygen saturation sensors are wirefess and very light, which
CA 02585824 2007-03-29
11
increase the mobility of the subject and minimizes the inference of data
recording
process and patient's sleeping
In the present invention the main sensor for recording respiratory
sounds is located on the trachea or inside the ear which has been found the
best
location for flow estimation purposes while in this patent respiratory sounds
are
recorded with a sensor positioned near patient's face. Also the present
sensors are
wireless sensors which decrease the movement noises and produce less
interrerence when subject is asleep.
Such a system reduces the need for polysomnography tests, hence
reducing the long waiting list for an accurate diagnostic assessment. The
apparatus
described hereinafter also facilitates studying patients with mobility or
behavioural
cognitive issues.
Long distance monitoring and diagnostic aid tools provide large
financial saving to both the health care system and families. The apparatus
described hereinafter provides a novel system to both developing a new and yet
simple diagnostic tool for sleep apnea disorder, and also a new way to connect
the
specialists and physicians with patients either in remote areas or even at
their
homes. Aside from its obvious benefd for covering the remote areas with equal
opportunity for health care, it also reduces the long waiting list for sleep
studies.
From a public health perspective, non-invasive and inexpensive methods to
determine airway responses across all ages and conditions present a major step
forward in the management of sleep apnea disorders.
CA 02585824 2007-03-29
12
The apparatus described hereinafter provides a portable and wireless
medical monitoring device/intelligent diagnostic system that enables
clinicians to
remotely and accurately diagnose sleep apnea at much less cost and which
greatly
reduces discomfort and inconvenience to the patient.
The apparatus described hereinafter can pave the way for a new line
of research and application that will simplify the measurement techniques to a
large
degree while enhancing the quality of symptomatic signs of the disease
detection
and helping an objective diagnosis.
The apparatus desctibed hereinafter provides a novel, integrated
diagnostic system to wirelessly acquire, de-noise, analyze tracheal
respiratory
sounds, estimate airflow acoustically, detect sleep apnea episodes, report the
duration and frequency of apnea, and use secure Internet-based networking
technologies to transfer data to a remote centralized clinical diagnostic
center.
BRIEF DESCRIPTION OF THE DRAWING~~
One embodiment of the invention will now be described in conjunction
with the accompanying drawings in which:
Figure 1 is a schematic illustration of a sleep apnea detection
apparatus according to the present invention,
Figure 2 is an illustration of a typical screen displaying the data to the
physician.
Figure 3(a) is a graphical representation of Tracheal sound entropy;
CA 02585824 2007-03-29
13
Figure 3(b) is a graphical representation of entropy after applying
nonlinear median fiiter (star marks represents the estimated apnea segments)
Figure 3(c) is a graphical representation of flow signal (solid line) along
with the estimated (dotted line) and real (dashed line) apnea segments for a
typical
subject.
Figure 4 is a graphical representation of Mean and standard deviation
values of errors in estimating apnea periods for different subjects
Figure 5 is a block diagram illustrating the adaptive filtering scheme for
removing the snoring sounds from the signal using the signal recorded by the
auxiliary microphone in the vicinity of the patient.
DETAILED DESCRIPTION
One of the reasons to record many signals in a sleep study is the
inaccuracy of those recorded signals in sleep apnea detection when they are
used
as a single measure. For example, nasal cannulae are used to measure airflow;
however, when the patient breathes through the mouth, the nasal cannulae
register
nothing and hence give a false positive detection error for apnea. Therefore,
combination of nasal pressure plus thermistor and End-tidal carbon dioxide
concentration in the expired air (ETC02) is used to have a qualitative measure
of
respiratory airflow. The abdominal movement recordings are mainly used to
detect
respiratory effort and hence to distinguish between central and obstructive
sleep
apnea. The ECG signals are also being used for detecting heart rate
variability and
another measure for apnea detection as well as monitoring patient's heart
c:ondition
CA 02585824 2007-03-29
14
during the night. The combination of EOG (Electrooculogram), EEG
(Electroencephalogram), and EMG (Electromyogram) signals are used for
assessing
the rapid eye movement (REM) sleep stage that is characterized by
desynchronization of the EEG and loss of muscle tone. Recording these signals
are
neeessary if insight in sleep quality is saught for diagnosis of certain sleep
disorders.
The most important inforrnation that doctors seek from a complete sleep study
Is the
duration and frequency of apnea andlor hypopnea and the blood's Oxygen
saturation (SaO2) level of the patient during the apnea. Oxygen level usually
drops
during the apnea and will rise quickly with awakenings. However, oximetry
alone
does not detect all cases of sleep apnea.
As the first and most important information of a sleep study is an
accurate measure of duration and frequency of apnea during sleep, the present
arrangement provides a fully automated system to detect apnea with only one
single
sensor that can also easily be applied by the patient at home and detect apnea
acoustically; hence reducing the need for a complete laboratory sleep study.
The apparatus provides an integrated system for remote and focai
monitoring and assessment of sleep apnea as a diagnostic aid for physicians
and
allows the following:
To record the $aOz data simultaneously with respiratory sound signals
through either a neck band with a microphone mounted in a chamber placed over
the supra-sternal notch, or by a microphone from inside the ear, followed by a
signal
conditioning unit.
CA 02585824 2007-03-29
To screen the raw data, separate snoring and other adventitious
sounds from breath sounds, estimate flow from the sounds and detect apnea
and/or
hypopnea episodes, determine the duration and frequency of the apnea episodes
and finally display the raw data, estimated airflow and display the estimated
eirflow
5 with marked detected apnea/hypopnea along with related information
(duration,
frequency and the corresponded SaQZ data).
Figure 1 shows the apparatus for steep apnea detection that can also
be used as a home-Care device while being connected to a clinical diagnostic
center
for online monitoring.
10 From a public health perspective, non-invasive and inexpensive
methods to determine airway responses across all ages and conditions would
present a major step forward in the management of sleep apnea disorders
The apparatus consists of six modules that permit sleep apnea
detection diagnosis. The clinical diagnosis can be performed either locally
(e.g. at a
15 clinical diagnostic center) andlor remotely (e.g. at clinician's
officelhome). The
apparatus will support several clinicians simultaneously carrying out clinical
work on
different patients. Likewise, patients can be monitored either locally (e.g.
at a clinical
diagnostic center) andlor remotely (e.g. at patient's home). The apparatus
will also
support many patients being concurrently monitored.
The apparatus has the foilowing modules
Collector module 10,
Transmitter module 11.
CA 02585824 2007-03-29
16
Organizer module 12,
Detector module 13,
Interface module 14, and
Manager module 15.
Collector module 10 captures physiological signals from different body
parts. The body parts include a microphone and transmitter 20 at the ear or
over the
neck by a wireless microphone mounted in chamber with a neckband for recording
sounds, a sensor 22 at the fingers of the patient for recording oximetry data
and an
extemal microphone 21 for recording sound from the environment around the
patient. Other signals can be detected in some cases from other body parts if
the
physicians request other biological signals, but this is not generally
intended herein.
The collector module locally transfers wirelessly the signals to the
Transmitter
module 11.
Transmitter module 11 receives biological signals from the Collector
module 10, securely transmits those signals and receives the signals at the
diagnostic center for its delivery to the Organizer module 12.
The Transmitter module 11 consists of two components: The
Transmitter Sender (S) and the Transmitter Receiver (R). The Transmitter
Sender
together with the Collector module resides at the patient location. The
Transmitter
Sender receives and store temporally signals from the Collector, and securely
and
reliably transfers the signals to the Transmitter Receiver. The Transmitter
Receiver
resides at the diagnostic center location. The Transmitter Receiver securely
and
CA 02585824 2007-03-29
17
reliably accepts the signals from the Transmitter Sender, and forwards the
signals to
the Organizer for the signal management and processing, There is one pair of
collector - transmitter modules per patient being monftored.
Inter-Transmitter components signal transmission can occurred locally
for those cases when the Collector-Transmitter Sender resides in the same
center
(e.g. at a diagnostic facility) or remotely for those cases when the Collector-
Transmitter Sender resides externally (e.g. at a patient home). The
transmission
can be wireless or wired (e.g. through the internetfintranet).
Organizer module 12 nsceives all captured signals from the 7ransmitter
module, organizes and classifies received signals per patient/physician and
prepares the signals for its processing by the Detector module. The Organizer
module simultaneously supports receiving many signals from different patients
that
is signals from collector - transmitter module pairs.
Detector module 13 pre-processes and analyzes the patient biological
signals, and performs the sleep apnea detection. The Detector performs snoring
sound detection and separation prior to the apnea/hypopnea detection. The
Detector has self-calibrated acoustical respiratory airflow estimation and
phase
detection utilized In respiratory and sleep apnea assessments.
Interface module 14 provides the graphical user interface to the
clinicians. The Interface module gives a secure, reliable, user-friendly,
interactive
access to the analysis performed by the Detector and it Is organized per
patient/physician. The Interface module consists of two main components: the
Interface Master (M) and the Interface Client (C). The Interface Master serves
the
CA 02585824 2007-03-29
18
information to the InterFace Client(s), while the Interface Client provides
the access
to the clinicians. Several Interface Clients can run concurrently giving out
results to
several clinicians. The Interface Client can be executed locally (e.g.
intranet) or
externaliy (e.g. internet).
Manager module 15 provides the application management functions. It
provides the graphical user interFace to the application administrator at the
diagnostic center location.
All system/application parameters are setup at the Manager module.
The system/application parameters c:onfigure the apparatus for its proper
operation.
The collector module microphone may comprise a neck band with a
microphone mounted in a chamber placed over the supra-stemal notch. However
the preferred arrangement as shown in Figure 1 schematically comprises a
wireless
microphone inside the ear or by a microphone mounted in a chamber with a neck
band to record respiratory sounds followed by a suitable signal conditioning
unit
depending on the type of the used sensor. The second sensor 21 collects sound
from the environment around. The third sensor 22 collects the conventional
Sa02
data or other oximetry data. The three sensors allow from the patient
simultaneous
data ar.quisition of the sound signals and the Sa02 data.
There are two options for recording respiratory sounds: using the ear
microphone or the neck microphone. The very small miniature ear microphone is
inserted into a piece of foam which has open ends and inserted to inside the
microphone. The small preamplifier of the microphone is placed behind the ear
CA 02585824 2007-03-29
19
similar to a hearing aid device. The ear microphone includes a wireless
transmitter
which is placed behind the ear, the miniature microphone and the foam for
securing
the microphone inside the ear. In case of neck microphone, it is inserted in a
chamber (with the size of a loony) which allows about 2 mm distance between
the
microphone and the skin when the chamber is placed over supra-stemal notch of
the
trachea of the patient with double sided adhesive ring tapes. The neck
microphone
will come with a neck band mainly for the comfort of the patient and also to
keep the
wire of the microphone free of touching the skin. In eittter case, the
preampiifier and
transmitter of the wireless microphone can be placed in the pocket of the
subject.
Aiternatively the whole element mounted in the ear canal includes the pre-
amplifier
and transmitter for complete wireless operation.
The detector module pre-processes and analyzes the recorded signal
in order to provide a user friendly, smart and interactive interface for the
physician as
a monitoring and diagnostic aid tool. The software in this part will de-noise
the
recorded sound, separate snoring sounds, estimate the flow acoustically,
detect
apnea and/or hypopnea episodes, count the duration and the frequency of their
occurrence, display the estimated flow with marked apnea episodes as shown in
Figure 3 along with the related information.
The respiratory sounds either from the ear or from the neck of the
patient will be recorded by a small wireless microphone. A Transmitter -
Sender
Module DSP board is designed to receive the analog signal, amplify and filter
the
CA 02585824 2007-03-29
signal, digitize it with a minimum of 5120 Hz sampling rate and store it as a
binary
file.
The Sa02 data simultaneously with the respiratory sounds is digitized
with 5120 Hz sampling rate and stored in a binary file for the entire duration
of the
5 sleep at the collector module.
The detector module signal processing of the sound signals has three
stages. First an automated aigorithm finds the artifacts (that normally appear
as
impulses in the signal) and removes them from further analyses. Secondly, the
snoring sounds, If they exist, are identified and separated from the
respiratory
10 sounds. Finally, from the cleaned respiratory sounds the entropy of the
signal is
calculated, the effect of heart sounds is removed, and apnea episodes are
detected
by the technique as described hereinafter. The average duration of the apnea
episodes, their frequency of occurrence and whether they are associated with
snoring, is presented as part of the information In the GUI interface for the
physioian.
15 Artifacts (usually due to movement) appear as very short duration
puises in the recorded signal. Wavelet analysis is a highly reliable method
with high
accuracy to automatically detect these artifacts. On the other hand, snoring
sounds
are musical sounds which appear with harmonic components in the spectrogram of
the recorded signal. Detection of snoring sounds is similar to detection of
crackle
20 sounds in the lung sounds. Multi-scale product of the wavelet co-efficient
is used to
detect and separate the snoring sounds. Techniques for the application of
digitai
CA 02585824 2007-03-29
21
signal processing techniques on biological signals including noise and
adventitious
sounds separation are known.
Onoe the respiratory sound signal is pre-processed and cleaned of
extra sounds, the entropy of the signal is calculated. As heart sounds have
overlap
with respiratory sounds at low frequencies and this is more pronounced at very
low
flow rate (the case of hypopnea), the effect of heart sounds has to be
cancelled from
the entropy or the range parameter of the signais prrior to apnea detection.
This is
described in more detail hereinafter.
Then, from the entropy or the range parameter of the signal, the apnea
episodes are identified using Otsu's thresholding method as described
hereinafter.
The flow estimation method as described hereinafter is enhanced to
make the method self-calibrated, That enables the apparatus to estimate the
actual
amount of flow. Finally, the episodes of hypopnea and apnea are marked; their
duration and frequency of occurrence during the entire sleep is presented on
the
interface module GUI display as a diagnostic aid to the physician.
Depending on the type of microphone used, both sounds result in the
same apnea detection episodes and flow estimation while the tuning of the
algorithm
for each sound signal requires slight modification, i.e. the threshold or the
parameters of the flow estimation model are different.
The apnea detection algorithm requires a snoring separation algorithm.
This can use one or more of the foliowing principles:
CA 02585824 2007-03-29
22
Applying Wavelet analysis to detect and mark the snoring sounds in
the time-frequency domain.
Applying the adaptn-e filter cancellation technique to remove the
snoring sounds from the signal using the signal reoorded by the auxiliary
microphone in the vicinity of the patient.
An automated algorithm can be provided to clean the recorded breath
sound signal from all extra plausible noises such as cough sounds, swallowing
sounds, vocal noise (in case the patient talks while dreaming), and artifacts
due to
movements foilowing the apnea detection aigorithm on the cleaned signal and
validate the results. These extraneous sounds will be removed using wavelet
analysis for localization and several different filter banks to remove each
type of
noises either automatically or at the user's command.
Di~
The interface module 14 provides a display of the detected
apneathypopnea episodes and related information for a clinician, The display
includes a display 30 of airflow versus time is plotted with apnea and
hypopnea
episodes marked on the screen.
The display includes oximetry data 31 plotted in association with the
estimated airflow.
The display has touch screen controls 32, 33 providing zoom-in and
zoom-out functions in the same window for both airflow and oximetry data
simultaneously.
CA 02585824 2007-03-29
23
The display is capable of playing the breathing and snoring sounds in
any zoomed-in or zoomed-out data window, that is the sounds are stored to
allow an
actual rendition of those sounds to the clinician to study the sounds at or
around an
apnea event.
The display is capable of displaying the extracted information about the
frequency and duration of apnea/hypopnea episodes, and their association with
the
level of oximetry data in a separate window for the clinician,
Aonea Detection
Referring now to Figures 4(a), 4(b) and 4(c), further detail of the Sleep
Apnea detection components is now described.
In order to smooth the calculated entropy or range parameter, it is
segmented into windows of 200ms with 50% overlap between adjacent windows.
Each window was then presented by its median value which is not sensitive to
jerky
fluctuation of the signal.
Next, the smoothed entropy or the smoothed range signal is classified
into two groups of breathing and apnea using a nonparametric and unsupervised
method for automatic threshold selection using the principles of OTSU.
In Otsu's method the threshold is chosen such that the variance
between classes is maximized. The between-class variance is defined as the sum
of varlances of all classes respect to the total mean value of all classes:
CA 02585824 2007-03-29
24
aa ~Wo(fla #r )_ +wJ (.a. -kr)Z. (~ )
where wj,A(i =1,2) are the probabiiity and mean values of the
classes, respectively and /.rr is the average of total values.
and the optimum threshold k" is selected so as:
crõ (k ) =~~ a (k)= (2)
The average of entropy or range values is another statistical measure
that can be used to detect apnea segments. In this study both the Otsu and the
average value of entropy or range value were used to define the classification
threshold as:
Tbr=min 4 (3)
where k' is the Otsu threshold and m is the average of the entropy or
range values.
Figure 4 presents (a) Tracheal sound entropy, (b) entropy after
applying nonlinear median filter (star marks represents the estimated apnea
segments) and c) flow signal (solid line) along with the estimated (dotted
line) and
real (dashed line) apnea segments for a typical subject. Comparing the results
depicted in Figure 4(a) and Fig. 4(b), the effect of applying median filter is
evident.
The star marks in Figure 4(b) show the estimated apnea segments. Investigating
CA 02585824 2007-03-29
the results depicted in Figure 4(o) it is clear that the proposed method
detects all the
apnea segments and classifies them correctly from the breath segments.
In this arrangement a new acoustical method for apnea detection is
proposed which is based on tracheal sound entropy or range value. The method
is
5 fast and easy to be implemented, which makes it suitable for on-line
applications.
Removal of snoring sounds by time-frequency filtering techniques may
have some problems due to the fact that snoring sounds also have strong low
frequency components, in which the acoustical apnea detection is based on. As
an
altemative, the snoring sounds can be recorded by another auxiliary microphone
in
10 the vicinity of the subject. This signal will not have breathing sounds and
can be
used as a noise reference. The apparatus then uses adaptive filtering for
noise
(snore) cancellation.
Snoring sounds are musical sounds which appear vrith harmonic
components in the spectrogram of the recorded signal. We record the snoring
15 sounds by an auxiliary microphone in the vicinity of the patient. Using the
source of
noise (recorded by the auxiliary microphone) adaptive filterring will cancel
the snoring
sounds from the breath and snoring sounds recorded over the neck or inside the
ear
of the patient.
Figure 5 illustrates the block diagram of the adaptive filtering scheme.
20 The filter has two inputs: the primary input and the reference signal, The
primary
input, x(t), (the microphone over the neck or inside the ear) contains an
interference,
m(n), (snoring sounds) along with the information bearing signal, b(n),
(tracheal
CA 02585824 2007-03-29
26
sound). The reference input, r(n), (the auxiliary microphone) represents a
version of
interference with undetectable information bearing (tracheal sounds) signal.
The
output of the RLS FIR filter, y(n), is close to the interferenoe component of
the
primary signal. Therefore, the output of the adaptive filter, e(n), is the
minimum mean square error estimate of the information bearing signal,
A
b(!1).
Computational demand of the smart, automated algorithm to run 8
hours of sleep data can be high. As an aiternative, the algorithms are written
in
C++ code that increases the speed of the algorithms compared to a high level
signal
processing software such as MATLAB. With fast, state-of-art new computers,
this
will not be a problem considering that this system will replace the 4 hours
labor work
of an sleep lab technician (the usual time to analyze one PSG patient's data)
with a
few minutes of processing time.
Flow Estimation
Flow calibration
In order to provide an effective flow estimation method it desirable to
provide another sound channel recorded over the lung to be used for
respiratory
phase detection and second it is desirable to provide one breath with known
flow
from the patient to calibrate (tune) the model to that patient. This
calibration (tuning)
is necessary because there is a wide variation of flow-sound relationship
between
the subjects due to their different chest size, lung capacity, gender, age,
etc.
CA 02585824 2007-03-29
27
Resviratorv ohase detection
Thus the present arrangement provides a method of respiratory phase
detection with only one channel breath sound (Tracheal sound signal).
In this method the patient is required to have a deep breath, hold it,
start the program and then exhale and keep breathing normally but with
different
flow rates from low to high for 30 seconds. This 30 second data that starts
with
expiration phase is used by the program to derive the necessary information
for
phase detection of the rest of breath sounds. The phase detection algorithm
is:
1. Sequester the 30 second initialization data into 100 ms
segments with 50% overlep between the successive segments.
2. Calculate the average power (in dB) of each segment over the
range of 150-450 Hz. The valleys of the resultant signal, which looks like a
rectified
sinusoid, determine the onsets of the breaths.
3. Knowing that the first phase is the exhalation, label the
initialization data as inspiration/expiration phases. Also by comparing the
max
power in each phase, label them as low, tidal and high flow rates.
4. Calculate the mean value of the average power (this time
calculated over the range of 500-1200 Hz of each segment) of the top 20% of
each
phase and store it for inspiration and expiration phases separately.
5. Calculate the ratio of the mean of the average power calculated
in Step 4 between the inspiration and expiration phases.
CA 02585824 2007-03-29
28
6. Apply this ratio as a threshold to the n.st of the data to
determine respiratory phases, For example, if the ratio of inspiration and
expiration
is calculated as 1.2, and the ratio of any known phase respect to the adjaaent
phase
(calculated with the same method) is equal to 0.8, it means that the first
known
phase is expiration and the second one is inspiration.
Automatic Self Calibration
Sinr,e having one breath with known flow defeats the purpose of
eliminating the flow measurement, in this arrangement is provided a method of
automaflc self calibration using a data bank. The concept includes a very
large data
bank of breathing sounds (tracheal sound) of people. This data bank is sorted
based on body-mess-index (BMI), age, gender, and smoking history of the
subjects.
This data is used to match the patient's BMI and other information to suggest
the
known flow-sound relationship required for calibration.
De-noisina and Adventitious Sound Removal
Since the patient might have some respiratory diseases that may
cause some adventitious sounds, i.e., crackle sounds or wheezes, an atgorithm
is
required to be run by the choiGe of the user (the clinician) to remove all
adventitious
sounds prior to flow estimation.
This algorithm has two parts: adventitious sound iocalization and
removal. For adventitious localization the arrangement herein uses multi-scale
(level 3) product of wavelet coefficients and applies a running threshold of
mean plus
three times of standard deviation to detect and localize the adventitious
sounds.
CA 02585824 2007-03-29
29
Then, the segments including artefacts will be removed in time-frequency
domain,
the signal will be interpolated by spline interpolation and the breath sound
signal will
be reconstructed in time domain by taking the inverse of the spectrogram.
Flow Estimation Using entr ov or ranoe parameter
1. Band-pass filter the tracheal sounds in the frequency range of
75 to 600 Hz and normalize the signal.
2. Sequester the band-pass fiitered signal into segments of 50 ms
(512 samples) with 75% overiap between the successive segments.
3. Let x(t) be the signal in each segment. The range value in each
segment can be defined as:
L, =1og[mean(x lx> Cmsx(x)' (1- r/ 100)]}- mean(x ( x<[max(x) * r/100D], (1)
where x is the tracheal sound signal in each window and meanQ is the average
value, and r = ] ,
or:
L, =1og[var(x)l, (2)
where var(x) is the variance of the signal in each segment.
4. The other feature that can be used for flow estimation is the entropy
of the signal in each segment. Let (Xl, =-=, XN ) represent the values of
signal x in
each segment. Estimate the probability density function (pdf) of signal x(t),
p(x), in
each window using the Normal kemel estimator:
fik(x)=N k ~~K(x ~ '
õ' (3)
CA 02585824 2007-03-29
where N is the number of samples (205), K is the Gaussian kemel function
( K(x) =(2ff )-t12 exp(-x2 /2) ) and h is the kemel bandwidth. For Gaussian
kernel the
optimum h is approximated as:
h,I, =1.066(x) N-D.z (4)
5 where a(x) is the estimated standard deviation of the signal x(t) in each
window.
Calculate the Shannon entropy in each segment:
N rY
Lf =-~Pelo$l['r) =
t-~
(5)
5. Use the modified linear model (6) to estimate flow from tracheal
10 sounds entropy or range (Eq. 1, 2 or 5) feature:
mean(L )
F ~ t = Cl p" Lph + CZ ,
mean(L--}
(6)
where C, and C2 are the model coefficients derived from the one breath with
known
flow, LPh =[L,,= - -, Lõ] is a vector representing the entropy or range value
of the signal
15 in the upper 40% values of each respiratory phase (inspiration or
expiration), w is
the number of segments in the upper 40% values of each respiratory phase and
L,
is the entropy or range values of tracheal sound in each segment (Eq. 1, 2 or
5).
Similarly, L,,,õe is the same vector that is calculated using the base
respiratory phase
signal. Base respiratory phase is the one breath that is assumed to be
available
20 with known flow to calibrate the model.
CA 02585824 2007-03-29
31
Heart sounds localization
1. Band-pass fiiter the tracheal sound records in the range of 75-
2500 Hz to remove motion artiFacts and high-frequency noises.
2. Divide the filtered signal into segments of 20 ms (205 samples)
with 50% overlap between successive segments.
3. Let x(t) be the signal in each segment. The range value in each
segment can be defined as:
Lr =1agCmean(x I x > [max(x) * (1- r / 100)D-mea4x I x < [max(x) * r /100D ],
(7)
where x is the tracheal sound signal in each window and mean() is the
average value, and r =t,
or:
Lr ffiIog[var(x)],
where var(x) is the variance of the signal in each segment.
The other feature that can be used for heart sounds localization is
entropy of the signal. Let {X,,===,Xh,} represent the values of signal x in
each
segment. Estimate the probability density function (pdf) of signal x(t), p(x),
in each
window using the Normal kemel estimator:
CA 02585824 2007-03-29
32
1 N 1
(x~X
K
Pk(x)~N~h y~ '
(g)
where N is the number of samples (205), K is the Gaussian kemel
function (K(x) =(21r)-"2 exp(-x2/2) ) and h is the kemel bandwidth. For
Gaussian
kernel the optimum h is approximated as:
hq,,, =1.06a(x)N4.z (10)
where a(x) is the estimated standard deviation of the signal x(t) in
each window,
4. Calculate the Shannon entropy in each segment:
N
H(P)=-~Pi 1og(Pr)= (11)
i=1
5. Define average plus standard deviation value (,t+a) of the
calculated entropy or range value as the threshold for heart sounds
localization.
6. Mark the segments with entropy or range values of higher than
this threshold as heart sounds-included segments.
Removing the effects of_ heart sounds
1. Localize heart sounds with the method mentioned above.
2. Calculate the range or entropy values for the segments void of
heart sounds.
3. Apply spline interpolation to estimate the values of the entropy
or range value in the segments including heart sounds. This technique
effectively
CA 02585824 2007-03-29
33
cancels the effect of heart sounds on the entropy or range values of the
tracheal
sound.
Example 1
Eight healthy subjects (3 males) aged 33.1f6.6 years with body mass
index of 23.3tls participated in this study, Tracheal sound was recorded using
Siemens accelerometer (EMT25C) placed over supra-sternal notch using double
adhesive tapes. Respiratory flow signal was measured by a mouth piece
pneumotachograph (Fleisch No.3) connected to a differentiai pressure
transducer
(Validyne, Northridge, CA). The subjects were instructed to breathe at very
shallow
flow rates with different periods of breath hold (2, 4, 6 sec) to simulate
apnea.
Tracheal sound and flow signals were recorded and digitized simultaneously at
a
10240 Hz sampling rate.
CA 02585824 2007-03-29
34
Feature Extraction
Among several features of tracheal sound such as the sound's mean
amplitude, average power and entropy used for flow estimation, entropy and the
range of signal have been shown to be the best features following fdnr
variation.
Therefore, in this study tracheal sounds entropy was used to detect apnea
(breath
hold in the experiments of this study) without the use of the measured flow
signal.
However, the recorded flow signal was used for validation of the acoustically
detected apnea.
Tracheal sound signal was band-pass filtered in the range of [75-600]
Hz, and then segmented into 50ms (512 samples) windows with 50% overlap
between the adjacent windows. In each window the tracheal sound probability
density function (pdf) was estimated based on kemel methods. Then, using the
method described earlier in this document Shannon entropy was calculated in
each
window that represents the changes in the signal's pdf. The effect of heart
sounds
which is most evident in the frequency range below 200 Hz was removed by the
method introduced earlier in this document.
Figure 3 shows the calculated entropy and its corresponding flow
signal for a typical subject. By comparing the signals depicted in Figure 1(a)
and
Figure 1(c) (solid line), it is evident that the values of entropy in the
breath hold
segments are smaller than those during breathing.
It should be noted that when localizing the segments including heart
sounds, it is nearly impossible to find out the exact boundaries of heart
sounds
CA 02585824 2007-03-29
segments. Therefore, there is always a trade off between the amount of heart
sounds interference in respiratory sounds and the amount of respiratory sounds
inforrnation missing during heart sounds cancefiation. The high peaks in the
calculated entropy (Figure 3a) are related to the heart sounds components
remained
5 in the tracheal sound. Figure 4 displays the mean and standard deviation
values of
length and lag errors in estimating apnea periods for different subjects.
Eam
In this study 10 healthy subjects of the previous participated. Subjects
were in two age groups: 5 adults (all female) 29+8 years old and 5 children (3
10 female) 9.6 1,7 years old. Respiratory sounds were recorded using Siemens
accelerometers (EMT25C) placed over supra-stemal notch and the upper right
lobe
lung. Respiratory flow was measured by a pneumotachograph (Fieisch No.3)
connected to a differential pressure transducer (Validyne, Northridge, CA).
Subjects
were instructed to breathe at 5 different flow rates with 5 breaths at each
target flow
15 followed by a 10s of breath hold at the end of experiment. In this study
the shallow
(< 6 mi/s/kg), low (6-9 mils/kg), medium (12-18 mi/slkg), high (18-27 ml/slkg)
and
very high (> 27 mi/slkg) target flow rates were investigated. Tracheal sound
signals
were used for flow estimation while the lung sound signal in correspondence
with
tracheal sound signal were used for respiratory phase detection. The onsets of
20 breaths were detected by running a threshold on the average power of the
tracheal
sounds and detecting the valleys of the signal. Since lung sounds are much
louder
during inspiration as opposed to expiration, then by comparing the average
power of
CA 02585824 2007-03-29
36
the lung and tracheal breath sounds it can easily and accurately be determined
which phases are inspiration or expiration.
As described above, the best performance for estimating flow from
tracheal sound entropy was achieved in the frequency range of 75-600 Hz.
Tracheal
sound signals were used for flow estimation while the lung sound signal in
correspondence with tracheal sound signal were used for respiratory phase
detection as mentioned above,
As described above, the best performance for estimating flow from
tracheal sound entropy was achieved in the frequency range of [75 600] Hz.
This is
in accordance with the fact that the main energy components of tracheal sound
exists in the frequency range below 600-800Hz. Thus, tracheal sound was band-
pass filtered in this range followed by segmenting the band-pass filtered
signal into
segments of 50 ms (512 samples) with 75% overlap between the successive
segments,
When studying tracheal sound in the frequency range below 300 Hz,
heart sounds are the main source of interference that changes the time and
frequency characteristics of the tracheal sound. Therefore, the presence of
heart
sounds will cause an error which can become significant in flow estimation in
very
shallow breathing, when most of the signal's energy is concentrated at low
frequencies. Hence, in this study the effect of heart sounds on the extracted
parameters was cancelled by using the same method as described above.
Since various modifications can be made in my invention as herein
CA 02585824 2007-03-29
37
above described, and many apparently widely different embodiments of same made
within the spirit and scope of the claims without department from such spirit
and
scope, it is intended that all matter contained in the accompanying
specification shall
be interpreted as illustrative only and not in a limiting sense.
